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利用动态贝叶斯网络进行多时相遥感变化检测

欧阳赟 马建文 戴芹

欧阳赟, 马建文, 戴芹. 利用动态贝叶斯网络进行多时相遥感变化检测[J]. 电子与信息学报, 2007, 29(3): 549-552. doi: 10.3724/SP.J.1146.2005.01023
引用本文: 欧阳赟, 马建文, 戴芹. 利用动态贝叶斯网络进行多时相遥感变化检测[J]. 电子与信息学报, 2007, 29(3): 549-552. doi: 10.3724/SP.J.1146.2005.01023
Ouyang Yun, Ma Jian-wen, Dai Qin. Multi-temporal Remote Sensing Change Detection Using Dynamic Bayesian Networks[J]. Journal of Electronics and Information Technology, 2007, 29(3): 549-552. doi: 10.3724/SP.J.1146.2005.01023
Citation: Ouyang Yun, Ma Jian-wen, Dai Qin. Multi-temporal Remote Sensing Change Detection Using Dynamic Bayesian Networks[J]. Journal of Electronics and Information Technology, 2007, 29(3): 549-552. doi: 10.3724/SP.J.1146.2005.01023

利用动态贝叶斯网络进行多时相遥感变化检测

doi: 10.3724/SP.J.1146.2005.01023
基金项目: 

国家863计划(2006AA12Z130)资助课题

Multi-temporal Remote Sensing Change Detection Using Dynamic Bayesian Networks

  • 摘要: 利用动态贝叶斯网络(DBNs)在处理不同时相遥感数据时可以一次性输入多个时间段的数据,同时完成分类和建立输出类别之间的关联。采用北京东部地区1994年、2001年和2003年5月份Landsat TM遥感数据进行实验,实验结果表明:基于DBNs的变化检测方法是遥感变化检测的一种新的有效方法,在遥感时序数据动态变化分析的研究方面也展示了巨大的发展潜力。
  • [1] Pearl J. Probabilistic Reasoning in Intelligent Systems. San Francisco, CA: Morgan Kaufmann. 1988, Chapter 3. [2] Heckerman D. A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, USA, 1995: 57. [3] 史忠植等. 知识发现. 北京: 清华大学出版社, 2002: 169-198. Murphy K P. Dynamic Bayesian Networks: Representation, inference and learning. PhD Thesis, UC Berkeley, 2002. [4] Pavlovic V, Rehg J M, Cham T J, and Murphy K P. A dynamic Bayesian network approach to figure tracking using learned dynamic models. Proceedings of IEEE International Conference on Computer Vision 1999, Corfu, Greece, 1999: 94-101. [5] Nefian A, Liang L, Pi X, Liu X, and Murphy K P. Dynamic Bayesian networks for audio-visual speech recognition. Journal of Applied Signal Processing, 2002, 11: 1-15. [6] Kwon J and Murphy K P. Modeling freeway traffic using coupled HMMs. Technical report, Department of Computer Science, UC Berkeley, 2000. [7] Zou M and Conzen S D. A new dynamic Bayesian network approach for identifying gene regulatory networks from time course microarray data[J].Bioinformatics.2005, 21(1):71-79 [8] Liang Bob. 未来处理器架构进行应用驱动的研究报告. Intel Microprocessor Research Forum. Beijing, China, 2002. [9] Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition[J].Proc. IEEE.1989, 77(2):257-286 [10] Bengio Y. Markovian models for sequential data. Neural Computing Surveys, 1999, 2: 129-162. [11] Roweis S and Ghahramani Z. A unifying review of linear Gaussian models[J].Neural Computation.1999, 11(2):305-345 [12] Minka T. From hidden Markov models to linear dynamical systems. Technical report, MIT, 1999. [13] 赵英时等. 遥感应用分析原理与方法. 北京: 科学出版社. 2003, 第7章. [14] 陈雪, 戴芹, 马建文,李小文. 贝叶斯网络分类算法在遥感数据变化检测上的应用. 北京师范大学学报(自然科学版), 2005, 41(1): 97-100. Chen Xue, Dai Qin, and Ma Jian-wen, et al. Application of Bayesian network classification to remote sensing change detection. Journal of Beijing Normal University (Natural Science), 2005, 41(1): 97-100. [15] 戴芹, 马建文, 欧阳赟. 利用贝叶斯网络进行遥感变化检测. 中国图像图形学报, 2005, 10(6): 705-709. [16] Dawid A P. Applications of a general propagation algorithm for probabilistic expert systems[J].Statistics and Computing.1992, 2:25-36 [17] Nilsson D. An efficient algorithm for finding the most probable configurations in probabilistic expert systems[J].Statistics and Computing.1998, 8:159-173 [18] Jensen F V. An introduction to Bayesian networks. London: UCL Press, 1996: 1-178. [19] Jensen F V, Olesen K G, and Andersen S K. An algebra of Bayesian belief universes for knowledge-based system[J].Networks.1990, 20:637-659
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出版历程
  • 收稿日期:  2005-08-18
  • 修回日期:  2006-01-11
  • 刊出日期:  2007-03-19

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